{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: http://mirrors.aliyun.com/pypi/simple/\n",
      "Collecting sklearn\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz (1.1 kB)\n",
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      "  Downloading http://mirrors.aliyun.com/pypi/packages/3f/de/5d5edccebf39c4bd4e6c153647939aaf126f725f585bb1c06ef46054ff89/scikit_learn-0.22.2.post1-cp38-cp38-manylinux1_x86_64.whl (7.0 MB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.11.0 in /root/anaconda3/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.18.1)\n",
      "Collecting joblib>=0.11\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/28/5c/cf6a2b65a321c4a209efcdf64c2689efae2cb62661f8f6f4bb28547cf1bf/joblib-0.14.1-py2.py3-none-any.whl (294 kB)\n",
      "\u001b[K     |████████████████████████████████| 294 kB 43.0 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting scipy>=0.17.0\n",
      "  Downloading http://mirrors.aliyun.com/pypi/packages/f3/08/8bdcdcd149ea41b655956feb7c19ebf7e1f561738bd5570b6ae015daf411/scipy-1.4.1-cp38-cp38-manylinux1_x86_64.whl (26.0 MB)\n",
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      "\u001b[?25hBuilding wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1315 sha256=a99ac3c7c9e8707bacd5cc93512c3ac3818c026cf969c2637443359fbb43b23d\n",
      "  Stored in directory: /root/.cache/pip/wheels/12/d0/6f/e8c6b1ceda61862da2451db91ad6aebd1c3da4cdc83df0fcb7\n",
      "Successfully built sklearn\n",
      "Installing collected packages: joblib, scipy, scikit-learn, sklearn\n",
      "Successfully installed joblib-0.14.1 scikit-learn-0.22.2.post1 scipy-1.4.1 sklearn-0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "dataset = load_iris()\n",
    "print(dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
      "       [4.9, 3. , 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.3, 0.2],\n",
      "       [4.6, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.6, 1.4, 0.2],\n",
      "       [5.4, 3.9, 1.7, 0.4],\n",
      "       [4.6, 3.4, 1.4, 0.3],\n",
      "       [5. , 3.4, 1.5, 0.2],\n",
      "       [4.4, 2.9, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.1],\n",
      "       [5.4, 3.7, 1.5, 0.2],\n",
      "       [4.8, 3.4, 1.6, 0.2],\n",
      "       [4.8, 3. , 1.4, 0.1],\n",
      "       [4.3, 3. , 1.1, 0.1],\n",
      "       [5.8, 4. , 1.2, 0.2],\n",
      "       [5.7, 4.4, 1.5, 0.4],\n",
      "       [5.4, 3.9, 1.3, 0.4],\n",
      "       [5.1, 3.5, 1.4, 0.3],\n",
      "       [5.7, 3.8, 1.7, 0.3],\n",
      "       [5.1, 3.8, 1.5, 0.3],\n",
      "       [5.4, 3.4, 1.7, 0.2],\n",
      "       [5.1, 3.7, 1.5, 0.4],\n",
      "       [4.6, 3.6, 1. , 0.2],\n",
      "       [5.1, 3.3, 1.7, 0.5],\n",
      "       [4.8, 3.4, 1.9, 0.2],\n",
      "       [5. , 3. , 1.6, 0.2],\n",
      "       [5. , 3.4, 1.6, 0.4],\n",
      "       [5.2, 3.5, 1.5, 0.2],\n",
      "       [5.2, 3.4, 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.6, 0.2],\n",
      "       [4.8, 3.1, 1.6, 0.2],\n",
      "       [5.4, 3.4, 1.5, 0.4],\n",
      "       [5.2, 4.1, 1.5, 0.1],\n",
      "       [5.5, 4.2, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.6, 1.4, 0.1],\n",
      "       [4.4, 3. , 1.3, 0.2],\n",
      "       [5.1, 3.4, 1.5, 0.2],\n",
      "       [5. , 3.5, 1.3, 0.3],\n",
      "       [4.5, 2.3, 1.3, 0.3],\n",
      "       [4.4, 3.2, 1.3, 0.2],\n",
      "       [5. , 3.5, 1.6, 0.6],\n",
      "       [5.1, 3.8, 1.9, 0.4],\n",
      "       [4.8, 3. , 1.4, 0.3],\n",
      "       [5.1, 3.8, 1.6, 0.2],\n",
      "       [4.6, 3.2, 1.4, 0.2],\n",
      "       [5.3, 3.7, 1.5, 0.2],\n",
      "       [5. , 3.3, 1.4, 0.2],\n",
      "       [7. , 3.2, 4.7, 1.4],\n",
      "       [6.4, 3.2, 4.5, 1.5],\n",
      "       [6.9, 3.1, 4.9, 1.5],\n",
      "       [5.5, 2.3, 4. , 1.3],\n",
      "       [6.5, 2.8, 4.6, 1.5],\n",
      "       [5.7, 2.8, 4.5, 1.3],\n",
      "       [6.3, 3.3, 4.7, 1.6],\n",
      "       [4.9, 2.4, 3.3, 1. ],\n",
      "       [6.6, 2.9, 4.6, 1.3],\n",
      "       [5.2, 2.7, 3.9, 1.4],\n",
      "       [5. , 2. , 3.5, 1. ],\n",
      "       [5.9, 3. , 4.2, 1.5],\n",
      "       [6. , 2.2, 4. , 1. ],\n",
      "       [6.1, 2.9, 4.7, 1.4],\n",
      "       [5.6, 2.9, 3.6, 1.3],\n",
      "       [6.7, 3.1, 4.4, 1.4],\n",
      "       [5.6, 3. , 4.5, 1.5],\n",
      "       [5.8, 2.7, 4.1, 1. ],\n",
      "       [6.2, 2.2, 4.5, 1.5],\n",
      "       [5.6, 2.5, 3.9, 1.1],\n",
      "       [5.9, 3.2, 4.8, 1.8],\n",
      "       [6.1, 2.8, 4. , 1.3],\n",
      "       [6.3, 2.5, 4.9, 1.5],\n",
      "       [6.1, 2.8, 4.7, 1.2],\n",
      "       [6.4, 2.9, 4.3, 1.3],\n",
      "       [6.6, 3. , 4.4, 1.4],\n",
      "       [6.8, 2.8, 4.8, 1.4],\n",
      "       [6.7, 3. , 5. , 1.7],\n",
      "       [6. , 2.9, 4.5, 1.5],\n",
      "       [5.7, 2.6, 3.5, 1. ],\n",
      "       [5.5, 2.4, 3.8, 1.1],\n",
      "       [5.5, 2.4, 3.7, 1. ],\n",
      "       [5.8, 2.7, 3.9, 1.2],\n",
      "       [6. , 2.7, 5.1, 1.6],\n",
      "       [5.4, 3. , 4.5, 1.5],\n",
      "       [6. , 3.4, 4.5, 1.6],\n",
      "       [6.7, 3.1, 4.7, 1.5],\n",
      "       [6.3, 2.3, 4.4, 1.3],\n",
      "       [5.6, 3. , 4.1, 1.3],\n",
      "       [5.5, 2.5, 4. , 1.3],\n",
      "       [5.5, 2.6, 4.4, 1.2],\n",
      "       [6.1, 3. , 4.6, 1.4],\n",
      "       [5.8, 2.6, 4. , 1.2],\n",
      "       [5. , 2.3, 3.3, 1. ],\n",
      "       [5.6, 2.7, 4.2, 1.3],\n",
      "       [5.7, 3. , 4.2, 1.2],\n",
      "       [5.7, 2.9, 4.2, 1.3],\n",
      "       [6.2, 2.9, 4.3, 1.3],\n",
      "       [5.1, 2.5, 3. , 1.1],\n",
      "       [5.7, 2.8, 4.1, 1.3],\n",
      "       [6.3, 3.3, 6. , 2.5],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [7.1, 3. , 5.9, 2.1],\n",
      "       [6.3, 2.9, 5.6, 1.8],\n",
      "       [6.5, 3. , 5.8, 2.2],\n",
      "       [7.6, 3. , 6.6, 2.1],\n",
      "       [4.9, 2.5, 4.5, 1.7],\n",
      "       [7.3, 2.9, 6.3, 1.8],\n",
      "       [6.7, 2.5, 5.8, 1.8],\n",
      "       [7.2, 3.6, 6.1, 2.5],\n",
      "       [6.5, 3.2, 5.1, 2. ],\n",
      "       [6.4, 2.7, 5.3, 1.9],\n",
      "       [6.8, 3. , 5.5, 2.1],\n",
      "       [5.7, 2.5, 5. , 2. ],\n",
      "       [5.8, 2.8, 5.1, 2.4],\n",
      "       [6.4, 3.2, 5.3, 2.3],\n",
      "       [6.5, 3. , 5.5, 1.8],\n",
      "       [7.7, 3.8, 6.7, 2.2],\n",
      "       [7.7, 2.6, 6.9, 2.3],\n",
      "       [6. , 2.2, 5. , 1.5],\n",
      "       [6.9, 3.2, 5.7, 2.3],\n",
      "       [5.6, 2.8, 4.9, 2. ],\n",
      "       [7.7, 2.8, 6.7, 2. ],\n",
      "       [6.3, 2.7, 4.9, 1.8],\n",
      "       [6.7, 3.3, 5.7, 2.1],\n",
      "       [7.2, 3.2, 6. , 1.8],\n",
      "       [6.2, 2.8, 4.8, 1.8],\n",
      "       [6.1, 3. , 4.9, 1.8],\n",
      "       [6.4, 2.8, 5.6, 2.1],\n",
      "       [7.2, 3. , 5.8, 1.6],\n",
      "       [7.4, 2.8, 6.1, 1.9],\n",
      "       [7.9, 3.8, 6.4, 2. ],\n",
      "       [6.4, 2.8, 5.6, 2.2],\n",
      "       [6.3, 2.8, 5.1, 1.5],\n",
      "       [6.1, 2.6, 5.6, 1.4],\n",
      "       [7.7, 3. , 6.1, 2.3],\n",
      "       [6.3, 3.4, 5.6, 2.4],\n",
      "       [6.4, 3.1, 5.5, 1.8],\n",
      "       [6. , 3. , 4.8, 1.8],\n",
      "       [6.9, 3.1, 5.4, 2.1],\n",
      "       [6.7, 3.1, 5.6, 2.4],\n",
      "       [6.9, 3.1, 5.1, 2.3],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [6.8, 3.2, 5.9, 2.3],\n",
      "       [6.7, 3.3, 5.7, 2.5],\n",
      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': '/root/anaconda3/lib/python3.8/site-packages/sklearn/datasets/data/iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "# data 为特征值\n",
    "data = dataset.data\n",
    "# target为分类类别\n",
    "target = dataset.target\n",
    "\n",
    "average_num = data.mean(axis = 0)\n",
    "\n",
    "import numpy as np\n",
    "data = np.array(data > average_num,dtype = \"int\")\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 随机获得训练和测试集\n",
    "def get_train_and_predict_set():\n",
    "    return train_test_split(data,target, random_state=14)\n",
    "data_train,data_predict,target_train,target_predict = get_train_and_predict_set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from collections import defaultdict\n",
    "from operator import itemgetter\n",
    "\n",
    "def train_feature(data_train,target_train,index,value):\n",
    "    \"\"\"\n",
    "        data_train:训练集特征\n",
    "        target_train:训练集类别\n",
    "        index:特征值的索引\n",
    "        value ：特征值\n",
    "    \"\"\"\n",
    "    count = defaultdict(int)\n",
    "    for sample,class_name in zip(data_train,target_train):\n",
    "        if(sample[index] ==value):\n",
    "            count[class_name] += 1\n",
    "            \n",
    "   \t# 进行排序\n",
    "    sort_class = sorted(count.items(),key=itemgetter(1),reverse = True)\n",
    "    # 拥有该特征最多的类别\n",
    "    max_class = sort_class[0][0]\n",
    "    max_num = sort_class[0][1]\n",
    "    all_num = 0\n",
    "    \n",
    "    for class_name,class_num in sort_class:\n",
    "        all_num += class_num\n",
    "#     print(\"{}特征，值为{}，错误数量为{}\".format(index,value,all_num-max_num))\n",
    "    # 错误率\n",
    "    error = 1 - (max_num / all_num)\n",
    "    return max_class,error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最好的特征是2\n",
      "[0, 2]\n"
     ]
    }
   ],
   "source": [
    "def train():\n",
    "    errors = defaultdict(int)\n",
    "    class_names = defaultdict(list)\n",
    "    # 遍历特征\n",
    "    for i in range(data_train.shape[1]):\n",
    "       # 遍历特征值 \n",
    "        for j in range(0,2):\n",
    "            class_name,error = train_feature(data_train,target_train,i,j)\n",
    "            errors[i] += error\n",
    "            class_names[i].append(class_name)            \n",
    "    return errors,class_names\n",
    "\n",
    "errors,class_names = train()\n",
    "# 进行排序\n",
    "sort_errors = sorted(errors.items(),key=itemgetter(1))\n",
    "best_error = sort_errors[0]\n",
    "\n",
    "best_feature = best_error[0]\n",
    "best_class = class_names[best_feature]\n",
    "print(\"最好的特征是{}\".format(best_error[0]))\n",
    "print(best_class)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测准确度65.78947368421053\n",
      "预测结果[0 0 0 2 2 2 0 2 0 2 2 0 2 2 0 2 0 2 2 2 0 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 2\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "# 进行预测\n",
    "def predict(data_test,feature,best_class):\n",
    "    return np.array([best_class[int(data[feature])] for data in data_test])\n",
    "\n",
    "result_predict = predict(data_predict,best_feature,best_class)\n",
    "\n",
    "print(\"预测准确度{}\".format(np.mean(result_predict == target_predict) * 100))\n",
    "\n",
    "print(\"预测结果{}\".format(result_predict))"
   ]
  }
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